Several computer vision tasks require capturing long-range dependencies between features.
For example, in order to recognize a teapot, it is necessary to identify and integrate several of its parts in their correct spatial relationship, e.g. a spout, a handle, a lid, while each part by itself is usually not enough to determine the object class.
Convolutional layers have proven very useful at several computer vision tasks such as classification, and they are at the core of most state-of-the-art neural networks [1]}.
A convolution transforms each pixel depending on a few neighboring pixels only, and the transformation is shared across all pixels.
The sparseness and sharing of the weights are among the reasons for the success of CNNs, since they provide an efficient parameterization; however, the small size of the neighbourhood implies that in a single layer, features at distant locations cannot be integrated.
In deep convolutional networks, the receptive field size increases sub-linearly with depth [2]}, therefore a large number of layers is necessary for integrating features across long distances.
